but i lost the picture, my bad. all i have is the synthetic version, i guess.

the algorithm used here for connections is based on a stochastic power law, making it a scale free network. the idea of hand-typing each connection in xml made my fingers cringe, so instead I wrote two python scripts and one superCollider function to help me synthesize this network.

i'm new at this whole python thing, as you can see. it's a really beautiful language though, visually. much easier on the eyes than superCollider, for this kind of stuff, anyway. so matrixToXML.py will take a file containing an adjacency matrix, line break delimited. so something like this:

this is a lot faster to write than xml, by the way. i wrote a superCollider function to write them for me, based on a simple algorithm.

the algorithm, while it may be somewhat strange, shows off the smalltalk-y beauty that is superCollider. it's way shorter than the following:

and finally, since this leaves you with a somewhat transparent naming scheme, i decided to encode a kind of orthographic heirarchy using a base-ten system.

i also used the same two python scripts to generate the xml for the other two networks. also i didn't eat them, because they were on paper.

a distributed network of surfaces in three-dimensional space.

a decentered network representing the lists of enrolled students in two ITP classes.